With the spread of electronic health records, increasingly large data repositories of clinical and other patient derived data are being built. These databases are large and difficult for any one specialist to analyze. To find the hidden associations within such data, we review methods for large-scale data-mining on electronic medical records, methods for text-mining of medical records, and methods for using controlled vocabularies for indexing unstructured content.
SCHEDULE: TUE, THU 1:30 PM - 2:50 PM
LOCATION: Skilling Auditorium (Fall 2015)
Discussion Section: Will now be in class (to accommodate SCPD students). The class lecture for that day will be made available as a video lecture.
TAs: David Moskowitz (dmosk AT stanford.edu), Sarah Poole (spoole AT stanford.edu), Vibhu Agarwal (vibhua AT stanford.edu)
The course has four modules. The first module will review the medical data miner’s tool-kit—including the use of ontologies in data-mining and healthcare utilization databases. The remaining modules will review three problem areas and computational methods used in that problem area via a set of 3-5 lectures ending in a “mini project” as home work. Each module will cover a new application area (e.g. drug safety surveillance, predictive analytics) and a new method (e.g. association rules, logistic regression). In addition, there are 8 discussion sections that provide in depth explanation of the methods referred to in the lectures. For 2015, these discussion sections will be recorded and available to SCPD (and remote) students.
The course will use real, de-identified, large size patient datasets (millions of patients range) that are made available for home work projects associated with the course.
This course is also offered in a 2 credit version (BIOMEDIN 225) which meets at the same time but requires only one home work.
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All homework assignments will be due before the start of lecture (2:15 pm) on the day the next homework is released.
older version when we had year end projects
Machine learning: an algorithmic perspective, Stephen Marsland
Introduction to the practice of statistics, David S. Moore, George P. McCabe
The Elements of Statistical Learning: Data Mining, Inference, and Prediction; Trevor Hastie, Robert Tibshirani and Jerome Friedman
Mining of Massive Datasets, Anand Rajaraman and Jeff Ullman
The Petabyte Age Because More Isn't Just More — More Is Different
The Unreasonable Effectiveness of Data
A few useful things to know about machine learning ← only works for on campus access. Same content in http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf